The atypical social brain network in autism: advances in structural and functional MRI studies

Risk of Autism After Pediatric Ischemic Stroke: A Nationwide Cohort Study

BACKGROUND AND OBJECTIVES

Ischemic stroke increases the risk of neurodevelopmental disorders; however, the risk of autism is not thoroughly explored. Our aim was to evaluate risk of autism and risk factors for autism in children with pediatric ischemic stroke and in their first-degree relatives.

METHODS

In this cohort study, individuals with ischemic stroke from 1969 to 2016, <18 years of age, alive 1 week after stroke, and without prior autism were identified in Swedish national registers. Ten matched controls per index individual and all first-degree relatives of index individuals and controls were identified. Conditional Cox regression was used to calculate the risk of autism. Unconditional logistic regression was performed to analyze sex, gestational age, age at stroke diagnoses, comorbid adverse motor outcome, comorbid epilepsy, and a sibling with autism as risk factors for autism in children with ischemic stroke.

RESULTS

Of the 1,322 index individuals, 46 (3.5%) were diagnosed with autism compared to 161 (1.2%) controls (adjusted hazard ratio [aHR] 3.02, 95% CI 2.15-4.25). There was no significant difference in risk of autism according to age at stroke: perinatal (aHR 2.69, 95% CI 1.44-5.03) and childhood stroke (aHR 3.18, 95% CI 2.12-4.78). The increased risk remained after exclusion of children born preterm or small for gestational age (aHR 3.78, 95% CI 2.55-5.60) and when children with stroke diagnosed from 1997 to 2014 were analyzed (aHR 2.91, 95% CI = 1.95-4.35). Compared to controls, the risk of autism was increased in individuals with ischemic stroke and comorbid epilepsy (aHR 7.05, 95% CI 3.74-13.30), as well as adverse motor outcome (aHR 4.28, 95% CI 2.44-7.51). When individuals with adverse motor outcome and epilepsy were censored, the risk of autism was still increased (aHR 2.37, 95% CI 1.45-3.85). Sex, gestational age, and having a sibling with autism were not associated with autism in individuals with pediatric ischemic stroke.

DISCUSSION

An increased risk of autism was seen after pediatric ischemic stroke, particularly in individuals with comorbid epilepsy, and could not be explained by being born preterm or small for gestational age. The risk was increased also in individuals free from epilepsy and adverse motor outcome, implying that all children with ischemic stroke should be readily screened for autism if the disorder is suspected.

The Altered Pattern of the Functional Connectome Related to Pathological Biomarkers in Individuals for Autism Spectrum Disorder Identification

Objective

Autism spectrum disorder (ASD) is a common neurodevelopmental disorder characterized by the development of multiple symptoms, with incidences rapidly increasing worldwide. An important step in the early diagnosis of ASD is to identify informative biomarkers. Currently, the use of functional brain network (FBN) is deemed important for extracting data on brain imaging biomarkers. Unfortunately, most existing studies have reported the utilization of the information from the connection to train the classifier; such an approach ignores the topological information and, in turn, limits its performance. Thus, effective utilization of the FBN provides insights for improving the diagnostic performance.

Methods

We propose the combination of the information derived from both FBN and its corresponding graph theory measurements to identify and distinguish ASD from normal controls (NCs). Specifically, a multi-kernel support vector machine (MK-SVM) was used to combine multiple types of information.

Results

The experimental results illustrate that the combination of information from multiple connectome features (i.e., functional connections and graph measurements) can provide a superior identification performance with an area under the receiver operating characteristic curve (ROC) of 0.9191 and an accuracy of 82.60%. Furthermore, the graph theoretical analysis illustrates that the significant nodal graph measurements and consensus connections exists mostly in the salience network (SN), default mode network (DMN), attention network, frontoparietal network, and social network.

Conclusion

This work provides insights into potential neuroimaging biomarkers that may be used for the diagnosis of ASD and offers a new perspective for the exploration of the brain pathophysiology of ASD through machine learning.

Resting state electroencephalography microstates in autism spectrum disorder: A mini-review

Atypical spatial organization and temporal characteristics, found via resting state electroencephalography (EEG) microstate analysis, have been associated with psychiatric disorders but these temporal and spatial parameters are less known in autism spectrum disorder (ASD). EEG microstates reflect a short time period of stable scalp potential topography. These canonical microstates (i.e., A, B, C, and D) and more are identified by their unique topographic map, mean duration, fraction of time covered, frequency of occurrence and global explained variance percentage; a measure of how well topographical maps represent EEG data. We reviewed the current literature for resting state microstate analysis in ASD and identified eight publications. This current review indicates there is significant alterations in microstate parameters in ASD populations as compared to typically developing (TD) populations. Microstate parameters were also found to change in relation to specific cognitive processes. However, as microstate parameters are found to be changed by cognitive states, the differently acquired data (e.g., eyes closed or open) resting state EEG are likely to produce disparate results. We also review the current understanding of EEG sources of microstates and the underlying brain networks.

Brain structural alterations in autism and criminal psychopathy

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Autism and psychopathy are both disorders of social cognition and share numerous of their features but still differ distinctively in their clinical phenotype.

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The lower grey matter volumes in the right temporal pole and the left inferior frontal gyrus are the most prominent findings distinguishing violent offenders with high psychopatic from ASD individuals.

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Violent offenders with high psychopatic traits and individuals with ASD both present similar lower grey matter volumes in the right precentral cortex compared to controls.

The goal of this study was to elucidate the anatomical brain basis of social cognition through two disorders with distinctively different phenotypes of social interaction. We compared structural MR images of 20 individuals with autism spectrum disorder (ASD), 19 violent offenders with high psychopathic traits, and 19 control participants using voxel-based morphometry (VBM). Our earlier study showed lower grey matter volume (GMV) values in the insula, frontal cortex, and sensorimotor cortex of the offender group compared to controls. In the present study, the images of the ASD group revealed lower GMV in the left precuneus, right cerebellum, and right precentral gyrus in comparison with controls. The comparison between the offender and ASD groups showed lower GMV values for the right temporal pole and left inferior frontal gyrus in the offender group. There was also an overlap of both disorders in the right pre-central cortex, showing lower GMV compared to controls. Our findings suggest structural differences between violent offenders with high psychopathy traits and ASD individuals in the frontotemporal social brain network areas, previously associated with empathy. We also provide evidence of similar abnormal structures in the motor cortex for both of these disorders, possibly related to uniting issues of social cognition.

Associations between the pupil light reflex and the broader autism phenotype in children and adults

The pupil light reflex (PLR), a marker of neuronal response to light, is a well-studied index of autonomic functioning. Studies have found that autistic children and adults have slower and weaker PLR responses compared to non-autistic peers, suggesting lower autonomic control. Altered autonomic control has also been associated with increased sensory difficulties in autistic children. With autistic traits varying in the general population, recent studies have begun to examine similar questions in non-autistic individuals. The current study looked at the PLR in relation to individual differences in autistic traits in non-autistic children and adults, asking how differences in the PLR could lead to variation in autistic traits, and how this might change across development. Children and adults completed a PLR task as a measure of sensitivity to light and autonomic response. Results showed that, in adults, increased levels of restricted and repetitive behaviors (RRB) were associated with a weaker and slower PLR. However, in children, PLR responses were not associated with autistic traits. Differences in PLR were also found across age groups, with adults showing smaller baseline pupil diameter and stronger PLR constriction as compared with children. The current study expanded on past work to examine the PLR and autistic traits in non-autistic children and adults, and the relevance of these findings to sensory processing difficulties is discussed. Future studies should continue to examine the neural pathways that might underlie the links between sensory processing and challenging behaviors.

High Intellectual Potential and High Functioning Autism: Clinical and Neurophysiological Features in a Pediatric Sample

High Intellectual Potential (HIP) and High Functioning Autism (HFA) are two different conditions sharing some clinical and neurobiological features. The aim of the present study was to characterize a sample of HIP children (n: 16; M/F: 14/2; median age: 10 years) in comparison to those with HFA (n: 17; M/F: 16/1; median age: 13 years) and to neurotypically developed (NTD) children (n: 10; M/F: 4/6; median age: 11 years) from a clinical and neurophysiological perspective. Specifically, a standardized clinical assessment of cognitive and adaptive skills, autistic symptoms, executive functions and behavioral features was performed. Moreover, event-related potentials (ERPs) were recorded, referring specifically to the mismatch negativity (MMN) and P300 paradigm. Our data highlighted the presence of similarities between the intellectually gifted individuals and the ones with autism (i.e., a nonhomogeneous intellective profile, an adaptive skills impairment, subthreshold autistic symptoms and increased perfectionism). Interestingly, a distinct neurophysiological characterization between groups came out, with evidence of a reduced MMN amplitude only in the HFA group. Furthermore, no differences within groups in the P300 component emerged. Therefore, our results start to provide a more informative characterization of the HIP phenotype in comparison to those of HFA and NTD, highlighting the potential role of the MMN amplitude index in helping clinicians and researchers to distinguish between HIP and HFA. Nevertheless, further research on the topic is strongly needed.

Looking Back at the Next 40 Years of ASD Neuroscience Research

During the last 40 years, neuroscience has become one of the most central and most productive approaches to investigating autism. In this commentary, we assemble a group of established investigators and trainees to review key advances and anticipated developments in neuroscience research across five modalities most commonly employed in autism research: magnetic resonance imaging, functional near infrared spectroscopy, positron emission tomography, electroencephalography, and transcranial magnetic stimulation. Broadly, neuroscience research has provided important insights into brain systems involved in autism but not yet mechanistic understanding. Methodological advancements are expected to proffer deeper understanding of neural circuitry associated with function and dysfunction during the next 40 years.

Prediction of Alzheimer's Disease Progression Based on Magnetic Resonance Imaging

The neuroimaging method of multimodal magnetic resonance imaging (MRI) can identify the changes in brain structure and function caused by Alzheimer's disease (AD) at different stages, and it is a practical method to study the mechanism of AD progression. This paper reviews the studies of methods and biomarkers for predicting AD progression based on multimodal MRI. First, different approaches for predicting AD progression are analyzed and summarized, including machine learning, deep learning, regression, and other MRI analysis methods. Then, the effective biomarkers of AD progression under structural magnetic resonance imaging, diffusion tensor imaging, functional magnetic resonance imaging, and arterial spin labeling modes of MRI are summarized. It is believed that the brain changes shown on MRI may be related to the cognitive decline in different prodrome stages of AD, which is conducive to the further realization of early intervention and prevention of AD. Finally, the deficiencies of the existing studies are analyzed in terms of data set size, data heterogeneity, processing methods, and research depth. More importantly, future research directions are proposed, including enriching data sets, simplifying biomarkers, utilizing multimodal magnetic resonance, etc. In the future, the study of AD progression by multimodal MRI will still be a challenge but also a significant research hotspot.

Involvement of the habenula in the pathophysiology of autism spectrum disorder

The habenula is a small epithalamic structure with widespread connections to multiple cortical, subcortical and brainstem regions. It has been identified as the central structure modulating the reward value of social interactions, behavioral adaptation, sensory integration and circadian rhythm. Autism spectrum disorder (ASD) is characterized by social communication deficits, restricted interests, repetitive behaviors, and is frequently associated with altered sensory perception and mood and sleep disorders. The habenula is implicated in all these behaviors and results of preclinical studies suggest a possible involvement of the habenula in the pathophysiology of this disorder. Using anatomical magnetic resonance imaging and automated segmentation we show that the habenula is significantly enlarged in ASD subjects compared to controls across the entire age range studied (6-30 years). No differences were observed between sexes. Furthermore, support-vector machine modeling classified ASD with 85% accuracy (model using habenula volume, age and sex) and 64% accuracy in cross validation. The Social Responsiveness Scale (SRS) significantly differed between groups, however, it was not related to individual habenula volume. The present study is the first to provide evidence in human subjects of an involvement of the habenula in the pathophysiology of ASD.

Longitudinal Prediction of Infant MR Images With Multi-Contrast Perceptual Adversarial Learning

The infant brain undergoes a remarkable period of neural development that is crucial for the development of cognitive and behavioral capacities (Hasegawa et al., 2018). Longitudinal magnetic resonance imaging (MRI) is able to characterize the developmental trajectories and is critical in neuroimaging studies of early brain development. However, missing data at different time points is an unavoidable occurrence in longitudinal studies owing to participant attrition and scan failure. Compared to dropping incomplete data, data imputation is considered a better solution to address such missing data in order to preserve all available samples. In this paper, we adapt generative adversarial networks (GAN) to a new application: longitudinal image prediction of structural MRI in the first year of life. In contrast to existing medical image-to-image translation applications of GANs, where inputs and outputs share a very close anatomical structure, our task is more challenging as brain size, shape and tissue contrast vary significantly between the input data and the predicted data. Several improvements over existing GAN approaches are proposed to address these challenges in our task. To enhance the realism, crispness, and accuracy of the predicted images, we incorporate both a traditional voxel-wise reconstruction loss as well as a perceptual loss term into the adversarial learning scheme. As the differing contrast changes in T1w and T2w MR images in the first year of life, we incorporate multi-contrast images leading to our proposed 3D multi-contrast perceptual adversarial network (MPGAN). Extensive evaluations are performed to assess the qualityand fidelity of the predicted images, including qualitative and quantitative assessments of the image appearance, as well as quantitative assessment on two segmentation tasks. Our experimental results show that our MPGAN is an effective solution for longitudinal MR image data imputation in the infant brain. We further apply our predicted/imputed images to two practical tasks, a regression task and a classification task, in order to highlight the enhanced task-related performance following image imputation. The results show that the model performance in both tasks is improved by including the additional imputed data, demonstrating the usability of the predicted images generated from our approach.

Repeated Sessions of Transcranial Direct Current Stimulation on Adolescents With Autism Spectrum Disorder: Study Protocol for a Randomized, Double-Blind, and Sham-Controlled Clinical Trial

Background: Social-emotional difficulties are a core symptom of autism spectrum disorder (ASD). Accordingly, individuals with ASD have problems with social cognition such as recognizing emotions from other peoples' faces. Various results from functional magnetic resonance imaging and electroencephalography studies as well as eye-tracking data reveal a neurophysiological basis of these deficits by linking them to abnormal brain activity. Thus, an intervention targeting the neural origin of ASD impairments seems warranted. A safe method able to influence neural activity is transcranial direct current stimulation (tDCS). This non-invasive brain stimulation method has already demonstrated promising results in several neuropsychiatric disorders in adults and children. The aim of this project is to investigate the effects of tDCS on ASD symptoms and their neural correlates in children and adolescents with ASD. Method: This study is designed as a double-blind, randomized, and sham-controlled trial with a target sample size of 20 male participants (aged 12-17 years) diagnosed with ASD. Before randomization, the participants will be stratified into comorbid depression, comorbid ADHS/conduct disorder, or no-comorbidity groups. The intervention phase comprises 10 sessions of anodal or sham tDCS applied over the left prefrontal cortex within 2 consecutive weeks. To engage the targeted brain regions, participants will perform a social cognition training during the stimulation. TDCS-induced effects on ASD symptoms and involved neural circuits will be investigated through psychological, neurophysiological, imaging, and behavioral data at pre- and post-measurements. Tolerability will be evaluated using a standardized questionnaire. Follow-up assessments 1 and 6 months after the intervention will examine long-lasting effects. Discussion: The results of this study will provide insights into the changeability of social impairments in ASD by investigating social and emotional abilities on different modalities following repeated sessions of anodal tDCS with an intra-simulation training. Furthermore, this trial will elucidate the tolerability and the potential of tDCS as a new treatment approach for ASD in adolescents. Clinical Trial Registration: The study is ongoing and has been registered in the German Registry of Clinical Trials (DRKS00017505) on 02/07/2019.

Glutamate and functional connectivity - support for the excitatory-inhibitory imbalance hypothesis in autism spectrum disorders

It has been proposed that the Glutamate (Glu) system is implicated in autism spectrum disorders (ASD) via an imbalance between excitatory and inhibitory brain circuits, which impacts on brain function. Here, we investigated the excitatory-inhibitory imbalance theory by measuring Glu-concentrations and the relationship with resting-state function. Nineteen adult males with ASD and 19 age and sex-matched healthy controls (HC) (23 - 58 years) underwent Proton Magnetic Resonance Spectroscopy of the dorsal anterior cingulate cortex (dACC) and resting-state functional Magnetic Resonance Imaging (fMRI). Glu and Glx concentrations were compared between groups. Seed-based functional connectivity was analyzed with a priori seeds of the right and left dACC. Finally, metabolite concentrations were related to functional connectivity coefficients and compared between both groups. Individuals with ASD showed significantly negative associations between increased Glx concentrations and reduced functional connectivity between the dACC and insular, limbic and parietal regions. In contrast, HC displayed a positive relationship between the same metabolite and connectivity measures. We provided new evidence to support the excitatory-inhibitory imbalance theory, where excitatory Glx concentrations were related to functional dysconnectivity in ASD. Future research is needed to investigate large-scale functional networks in association with both excitatory and inhibitory metabolites in subpopulations of ASD.

The relationships between the topological properties of the whole-brain white matter network and the severity of autism spectrum disorder: A study from monozygotic twins

Twins provide a valuable perspective for exploring the pathological mechanism of autism spectrum disorder (ASD). We aim to analyze differences in the topological properties of the white matter (WM) network between monozygotic twins with ASD (MZCo-ASD) and children with typical development (TD). We enrolled 67 subjects aged 2-9 years. Twenty-three pairs of MZCo-ASD and 21 singleton children with TD completed clinical assessments and diffusion tensor imaging (DTI). Graph theory was used to compare the topological properties of the WM network between the two groups, and analyzed their correlations with the severity of clinical symptoms. We found that the global efficiency (Eg) of MZCo-ASD is weaker than that of TD children, while the shortest path length (Lp) of MZCo-ASD is longer than that of TD children, and MZCo-ASD have three unique hubs (the bilateral dorsolateral superior frontal gyrus and right insula). Eg and Lp were both correlated with the repetitive behavior scores of the Autism Diagnostic Interview-Revised (ADI-R) in the MZCo-ASD group, and the nodal efficiency of the dorsal superior frontal gyrus (SFGdor) was correlated with the ADI-R scores of repetitive behaviors. Left SFGdor nodal efficiency was correlated with Repetitive Behavior and Communication, two core symptoms of autism. The results implicated that MZCo-ASD had atypical brain structural network attributes and node distributions. Using MZCo-ASD, we found that the WM topological properties that correlate with the severity of ASD core symptoms were Eg, Lp, and the nodal efficiency of the SFGdor.

Altered Thalamocortical Connectivity in 6-Week-Old Infants at High Familial Risk for Autism Spectrum Disorder

Converging evidence from neuroimaging studies has revealed altered connectivity in cortical-subcortical networks in youth and adults with autism spectrum disorder (ASD). Comparatively little is known about the development of cortical-subcortical connectivity in infancy, before the emergence of overt ASD symptomatology. Here, we examined early functional and structural connectivity of thalamocortical networks in infants at high familial risk for ASD (HR) and low-risk controls (LR). Resting-state functional connectivity and diffusion tensor imaging data were acquired in 52 6-week-old infants. Functional connectivity was examined between 6 cortical seeds-prefrontal, motor, somatosensory, temporal, parietal, and occipital regions-and bilateral thalamus. We found significant thalamic-prefrontal underconnectivity, as well as thalamic-occipital and thalamic-motor overconnectivity in HR infants, relative to LR infants. Subsequent structural connectivity analyses also revealed atypical white matter integrity in thalamic-occipital tracts in HR infants, compared with LR infants. Notably, aberrant connectivity indices at 6 weeks predicted atypical social development between 9 and 36 months of age, as assessed with eye-tracking and diagnostic measures. These findings indicate that thalamocortical connectivity is disrupted at both the functional and structural level in HR infants as early as 6 weeks of age, providing a possible early marker of risk for ASD.

Could Gait Biomechanics Become a Marker of Atypical Neuronal Circuitry in Human Development?-The Example of Autism Spectrum Disorder

This perspective paper presents converging recent knowledge in neurosciences (motor neurophysiology, neuroimaging and neuro cognition) and biomechanics to outline the relationships between maturing neuronal network, behavior, and gait in human development. Autism Spectrum Disorder (ASD) represents a particularly relevant neurodevelopmental disorder (NDD) to study these convergences, as an early life condition presenting with sensorimotor and social behavioral alterations. ASD diagnosis relies solely on behavioral criteria. The absence of biological marker in ASD is a main challenge, and hampers correlations between behavioral development and standardized data such as brain structure alterations, brain connectivity, or genetic profile. Gait, as a way to study motor system development, represents a well-studied, early life ability that can be characterized through standardized biomechanical analysis. Therefore, developmental gait biomechanics might appear as a possible motor phenotype and biomarker, solid enough to be correlated to neuronal network maturation, in normal and atypical developmental trajectories—like in ASD.

Early Diagnostics and Early Intervention in Neurodevelopmental Disorders-Age-Dependent Challenges and Opportunities

This review discusses early diagnostics and early intervention in developmental disorders in the light of brain development. The best instruments for early detection of cerebral palsy (CP) with or without intellectual disability are neonatal magnetic resonance imaging, general movements assessment at 2-4 months and from 2-4 months onwards, the Hammersmith Infant Neurological Examination and Standardized Infant NeuroDevelopmental Assessment. Early detection of autism spectrum disorders (ASD) is difficult; its first signs emerge at the end of the first year. Prediction with the Modified Checklist for Autism in Toddlers and Infant Toddler Checklist is possible to some extent and improves during the second year, especially in children at familial risk of ASD. Thus, prediction improves substantially when transient brain structures have been replaced by permanent circuitries. At around 3 months the cortical subplate has dissolved in primary motor and sensory cortices; around 12 months the cortical subplate in prefrontal and parieto-temporal cortices and cerebellar external granular layer have disappeared. This review stresses that families are pivotal in early intervention. It summarizes evidence on the effectiveness of early intervention in medically fragile neonates, infants at low to moderate risk, infants with or at high risk of CP and with or at high risk of ASD.

Molecular Pathways within Autism Spectrum Disorder Endophenotypes

Autism spectrum disorder (ASD) is a condition that includes a number of neurodevelopmental mental disorders. Recent genetic/genomic investigations have reported an increased prevalence of copy number variations (CNVs) in individuals with autism. Despite the extensive evidence of a genetic component, the genes involved are not known and the background is heterogeneous among subjects. As such, it is highly likely that multiple events (molecular cascades) are implicated in the development of autism. The aim of this work was to shed some light on the biological background behind this condition. We hypothesized that the heterogeneous alterations found within different individuals may converge into one or more specific biological functions (pathways) linked to the heterogeneous phenotypes commonly observed in subjects with ASD. We analyzed a sample of 107 individuals for CNV alterations and checked the genes located within the altered loci (1366). Then, we characterized the subjects for distinct phenotypes. After creating subsamples based on symptoms, the CNVs related to each specific symptom were used to create distinct networks associated with each phenotype (18 in total in the sample under analysis). These networks were independently clustered and enriched to identify potential common pathways involved in autism and variably combined with the clinical phenotype. The first 10 pathways of the analysis are discussed.

Social Cognition in a Research Domain Criteria Perspective: A Bridge Between Schizophrenia and Autism Spectra Disorders

Schizophrenia and autism spectra disorders are currently conceptualized as distinct clinical categories. However, the relationship between these two nosological entities has been revisited in recent years due to the evidence that they share some important clinical and neurobiological features, putting into question the nature and the extent of their commonalities and differences. In this respect, some core symptoms that are present in both disorders, such as social cognitive deficits, could be a primary target of investigation. This review briefly summarizes the commonalities and overlapping features between schizophrenia and autism spectra disorders in social cognitive functions, considering this construct in a Research Domain Criteria perspective. The clinical manifestation of deficits in social cognition are similar in schizophrenia spectrum disorders and autism spectrum disorders, and brain areas that appear to be altered in relation to these impairments are largely shared; however, the results of various studies suggest that, in some cases, the qualitative nature of these alterations may be different in the two spectra. Moreover, relevant differences could be present at the level of brain networks and connections. More research is required in this field, regarding molecular and genetic aspects of both spectra, to better define the neurobiological mechanisms involved in social cognition deficits, with the objective of developing specific and targeted treatments.

Gender-Related and Hemispheric Effects in Cortical Thickness-Based Hemispheric Brain Morphological Network

Objective

The current study examined gender-related differences in hemispheric asymmetries of graph metrics, calculated from a cortical thickness-based brain structural covariance network named hemispheric morphological network.

Methods

Using the T1-weighted magnetic resonance imaging scans of 285 participants (150 females, 135 males) retrieved from the Human Connectome Project (HCP), hemispheric morphological networks were constructed per participant. In these hemispheric morphologic networks, the degree of similarity between two different brain regions in terms of the distributed patterns of cortical thickness values (the Jensen–Shannon divergence) was defined as weight of network edge that connects two different brain regions. After the calculation and summation of global and local graph metrics (across the network sparsity levels K = 0.10‐0.36), asymmetry indexes of these graph metrics were derived.

Results

Hemispheric morphological networks satisfied small-worldness and global efficiency for the network sparsity ranges of K = 0.10–0.36. Between-group comparisons (female versus male) of asymmetry indexes revealed opposite directionality of asymmetries (leftward versus rightward) for global metrics of normalized clustering coefficient, normalized characteristic path length, and global efficiency (all p < 0.05). For the local graph metrics, larger rightward asymmetries of cingulate-superior parietal gyri for nodal efficiency in male compared to female, larger leftward asymmetry of temporal pole for degree centrality in female compared to male, and opposite directionality of interhemispheric asymmetry of rectal gyrus for degree centrality between female (rightward) and male (leftward) were shown (all p < 0.05).

Conclusion

Patterns of interhemispheric asymmetries for cingulate, superior parietal gyrus, temporal pole, and rectal gyrus are different between male and female for the similarities of the cortical thickness distribution with other brain regions. Accordingly, possible effect of gender-by-hemispheric interaction has to be considered in future studies of brain morphology and brain structural covariance networks.

Morphofunctional Alterations of the Hypothalamus and Social Behavior in Autism Spectrum Disorders

An accumulating body of evidence indicates a tight relationship between the endocrine system and abnormal social behavior. Two evolutionarily conserved hypothalamic peptides, oxytocin and arginine-vasopressin, because of their extensively documented function in supporting and regulating affiliative and socio-emotional responses, have attracted great interest for their critical implications for autism spectrum disorders (ASD). A large number of controlled trials demonstrated that exogenous oxytocin or arginine-vasopressin administration can mitigate social behavior impairment in ASD. Furthermore, there exists long-standing evidence of severe socioemotional dysfunctions after hypothalamic lesions in animals and humans. However, despite the major role of the hypothalamus for the synthesis and release of oxytocin and vasopressin, and the evident hypothalamic implication in affiliative behavior in animals and humans, a rather small number of neuroimaging studies showed an association between this region and socioemotional responses in ASD. This review aims to provide a critical synthesis of evidences linking alterations of the hypothalamus with impaired social cognition and behavior in ASD by integrating results of both anatomical and functional studies in individuals with ASD as well as in healthy carriers of oxytocin receptor (OXTR) genetic risk variant for ASD. Current findings, although limited, indicate that morphofunctional anomalies are implicated in the pathophysiology of ASD and call for further investigations aiming to elucidate anatomical and functional properties of hypothalamic nuclei underlying atypical socioemotional behavior in ASD.

Visual Perception in Autism Spectrum Disorder: A Review of Neuroimaging Studies

Although autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by social impairments, patients with ASD frequently manifest atypical sensory behaviors. Recently, atypical sensory perception in ASD has received much attention, yet little is known about its cause or neurobiology. Herein, we review the findings from neuroimaging studies related to visual perception in ASD. Specifically, we examined the neural underpinnings of visual detection, motion perception, and face processing in ASD. Results from neuroimaging studies indicate that atypical visual perception in ASD may be influenced by attention or higher order cognitive mechanisms, and atypical face perception may be affected by disrupted social brain network. However, there is considerable evidence for atypical early visual processing in ASD. It is likely that visual perceptual abnormalities are independent of deficits of social functions or cognition. Importantly, atypical visual perception in ASD may enhance difficulties in dealing with complex and subtle social stimuli, or improve outstanding abilities in certain fields in individuals with Savant syndrome. Thus, future research is required to elucidate the characteristics and neurobiology of autistic visual perception to effectively apply these findings in the interventions of ASD.

Autism Spectrum Disorder and Narcolepsy: A Possible Connection That Deserves to Be Investigated

Narcolepsy in childhood-adolescence is characterized by a high occurrence of psychiatric comorbidities. The most frequent psychiatric disorders reported in these patients are attention deficit/hyperactivity disorder, depression, anxiety disorder, and schizophrenia. However, narcolepsy can be associated also with introversion, sorrowfulness, feelings of inferiority, impaired affectivity modulation, emotional lability, irritability, aggressiveness, and poor attention, that have been pooled by some authors under a definition of "narcoleptic personality." Some aspects of this "narcoleptic personality," and in particular introversion, impaired affectivity modulation, irritability, and poor attention, partially overlap with the clinical features of the individuals with autism spectrum disorder, considering also those that are not regarded as core autism symptoms. Till now, in literature the number of cases affected by both narcolepsy and autism spectrum disorder (seven patients) has been clearly too small to demonstrate the presence of a pathogenetic link between these two conditions, but this possible connection has not yet been adequately investigated, despite the presence of several points in common. The finding of a connection between narcolepsy and autism spectrum disorder could boost the study of possible etiopathogenetic mechanisms shared between these two apparently so distant disorders. Basing on the literature data summarized in this paper, in the diagnostic work-up of a child with narcolepsy it is essential to evaluate also the social-communicative behavior using standardized tools in order to detect the real recurrence of clinical features suggesting an autism spectrum disorder. At the same time, it appears necessary to screen in the individuals with autism spectrum disorder for the possible presence of evoking symptoms of narcolepsy.

Neurocognitive Mechanisms Underlying Social Atypicalities in Autism: Weak Amygdala's Emotional Modulation Hypothesis

Autism spectrum disorder (ASD) is a neurodevelopmental condition associated with atypicalities in social interaction. Although psychological and neuroimaging studies have revealed divergent impairments in psychological processes (e.g., emotion and perception) and neural activity (e.g., amygdala, superior temporal sulcus, and inferior frontal gyrus) related to the processing of social stimuli, it remains difficult to integrate these findings. In an effort to resolve this issue, we review our psychological and functional magnetic resonance imaging (fMRI) findings and present a hypothetical neurocognitive model. Our psychological study showed that emotional modulation of reflexive joint attention is impaired in individuals with ASD. Our fMRI study showed that modulation from the amygdala to the neocortex during observation of dynamic facial expressions is reduced in the ASD group. Based on these findings and other evidence, we hypothesize that weak modulation from the amygdala to the neocortex-through which emotion rapidly modulates various types of perceptual, cognitive, and motor processing functions-underlies the social atypicalities in individuals with ASD.

Atypical Amygdala-Neocortex Interaction During Dynamic Facial Expression Processing in Autism Spectrum Disorder

Atypical reciprocal social interactions involving emotional facial expressions are a core clinical feature of autism spectrum disorder (ASD). Previous functional magnetic resonance imaging (fMRI) studies have demonstrated that some social brain regions, including subcortical (e.g., amygdala) and neocortical regions (e.g., fusiform gyrus, FG) are less activated during the processing of facial expression stimuli in individuals with ASD. However, the functional networking patterns between the subcortical and cortical regions in processing emotional facial expressions remain unclear. We investigated this issue in ASD (n = 31) and typically developing (TD; n = 31) individuals using fMRI. Participants viewed dynamic facial expressions of anger and happiness and their corresponding mosaic images. Regional brain activity analysis revealed reduced activation of several social brain regions, including the amygdala, in the ASD group compared with the TD group in response to dynamic facial expressions vs. dynamic mosaics (p < 0.05, ηp2 = 0.19). Dynamic causal modeling (DCM) analyses were then used to compare models with forward, backward, and bi-directional effective connectivity between the amygdala and neocortical networks. The results revealed that: (1) the model with effective connectivity from the amygdala to the neocortex best fit the data of both groups; and (2) the same model best accounted for group differences. Coupling parameter (i.e., effective connectivity) analyses showed that the modulatory effects of dynamic facial processing were substantially weaker in the ASD group than in the TD group. These findings suggest that atypical modulation from the amygdala to the neocortex underlies impairment in social interaction involving dynamic facial expressions in individuals with ASD.